Physician directed radiation treatment planning

ABSTRACT

The treatment planning engine empowers radiation treatment decision makers, such as a physician, to efficiently identify effective radiation treatment outcomes for a given patient during the contouring stage. Specifically, using the treatment planning engine, the physician may iteratively and in real-time evaluate different treatment outcomes for a patient before selecting an optimal outcome that will guide the delivery of radiation treatment to the patient. By providing real-time information as to potential toxicity and treatment efficacy during the contouring stage, the physician is empowered to make informed decisions at the preliminary contouring stage.

CROSS REFERENCE TO RELATED APPLICATIONS

This application claims the benefit of U.S. Provisional Application No.62/073,711, filed Oct. 31, 2014, which is incorporated by reference inits entirety.

BACKGROUND

This invention relates generally to radiation treatment planning.

Providing radiation therapy to patients diagnosed with cancer includescreating a radiation treatment outcome. Often, where the cancer islocalized in the patient's anatomy, such as in a tumor, the creation ofthe radiation treatment outcome involves solving a difficult geometricproblem and/or making judgment calls related to the total radiation doseor total dose received by the tumor and nearby healthy tissue.Therefore, creating the radiation treatment outcome can be a timeconsuming process that involves multiple medical personnel providingmultiple iterations of the treatment outcome over many days, which mayincrease the time from diagnosis of the cancer to treatment of thecancer. In particular, the dosimetrist who creates the treatment outcomehas a difficult challenge in creating the optimal plan due to theoverwhelming degrees of freedom and constraint priorities. Further,physicians, who should be guiding the dosimetrist during the treatmentplanning, are often disconnected from the treatment planning processbecause of other demands on their time. Consequently, the modernradiation treatment planning process is time-consuming and oftenproceeds without the sufficient involvement of the physician treatingthe patient.

SUMMARY

A preliminary stage in radiation treatment planning for a given patientinvolves a physician defining the contours of the tumor volume andanatomical structures located near the tumor volume. The treatmentplanning engine described herein empowers radiation treatment decisionmakers, such as a physician, to efficiently identify an effectiveradiation treatment outcome for a given patient during the contouringstage. Specifically, the treatment planning engine stores treatmentoutcomes resulting from radiation treatment delivered to patients overtime. Using the treatment planning engine, the physician may iterativelyand in real-time evaluate different treatment outcomes of patientsdetermined to be similar to the current patient before selecting anoptimal plan for the patient. By providing real-time information as topotential toxicity and treatment efficacy during the contouring stage,the physician is empowered to make informed decisions at the preliminarycontouring stage.

In operation, the treatment planning engine enables the physician toeasily define contours of tumor volumes and nearby anatomical structuresof a given patient. Based on the defined contours, the treatmentplanning engine identifies treatment outcomes resulting from previouslydelivered radiation treatment that may be effective for the givenpatient and provides those treatment outcomes to the physician forevaluation. Each treatment outcome specifies the dose of radiation thatwas delivered to a tumor volume and/or the nearby anatomical structures.Upon evaluating the identified treatment outcomes, the physician mayselect one of the outcomes for the patient or may re-define the contoursof the tumor volume and nearby anatomical structures such that thetreatment planning engine identifies different treatment outcomesaccording to the new contours. In such a manner, the treatment planningengine allows the physician to iteratively modify the contours until atreatment outcome that delivers the optimal radiation treatment to thepatient is identified.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a system environment for radiationtreatment planning and delivery, in accordance with an embodiment of theinvention.

FIG. 2 is an exemplary user interface for defining contours on patientimage data, in accordance with an embodiment of the invention.

FIG. 3 is an exemplary user interface for visualizing recommendedtreatment outcomes, in accordance with an embodiment of the invention.

FIG. 4 is a state diagram illustrating the various stages of radiationtreatment planning and delivery, in accordance with an embodiment of theinvention.

FIG. 5 is a flow diagram illustrating the steps for physician directedradiation treatment planning, in accordance with an embodiment of theinvention.

FIG. 6 is a block diagram illustrating components of an example machineconfigured to read instructions from a machine-readable medium andexecute the instructions in a processor (or controller), in accordancewith an embodiment of the invention.

The figures depict various embodiments of the present invention forpurposes of illustration only. One skilled in the art will readilyrecognize from the following discussion that alternative embodiments ofthe structures and methods illustrated herein may be employed withoutdeparting from the principles of the invention described herein.

DETAILED DESCRIPTION System Architecture

FIG. 1 is a block diagram of a system environment 100 for radiationtreatment planning and delivery, in accordance with an embodiment of theinvention. As shown, the system environment 100 includes an imagingengine 102, a treatment planning engine 104, and a treatment deliveryengine 106.

The imaging engine 102 provides imaging data associated with patientsneeding radiation treatment. The imaging data may include visualrepresentations of the interior of a patient's body for medicalpurposes. In one embodiment, the imaging engine 102 generates the visualrepresentation through one or more scanning techniques, such as computedtomography (CT), nuclear medicine including positron emission tomography(PET), and magnetic resonance imaging (MRI). In alternative embodiments,the imaging engine 102 receives the imaging data from external sourcesand stores the imaging data in an internal data base. The imaging engine102 transmits the imaging data associated with the patient to thetreatment planning engine 104 for the purposes of generating a radiationtreatment outcome for the patient.

The treatment planning engine 104 processes imaging data associated witha patient and recommends different treatment outcomes to the patient'sphysician, thus enabling the physician to efficiently identify theoptimal treatment outcome for the patient. The treatment planning engine104 includes a patient data module 108, a notification module 110, acontour definition module 112, an outcome recommendation module 114, auser interface (UI) module 116, a patient data store 118, and an outcomestore 120.

The patient data store 118 and the outcome store 120 may each be, orinclude, one or more tables, one or more relational databases, and/orone or more multi-dimensional data cubes. Further, though illustrated asa single component, the patient data store 118 and the outcome store 120may each be a plurality of databases, such as a database cluster, whichmay be implemented on a single computing device or distributed between anumber of computing devices or memory components. Further, the variousmodules and data stores included in the treatment planning engine 104may be physically co-located within one computing system or,alternatively, may be disparately located across multiple computingsystems.

The patient data module 108 manages patient data and stores such data inthe patient data store 118. In one embodiment, each patient has a uniqueidentifier such that the patient data belonging to a given patient isstored in conjunction with the unique identifier. Patient data includes,but is not limited to, imaging data received from the imaging engine102, electronic medical records (EMRs) associated with the patient,information related to the patient's treatment team (e.g., physician),and the treatment machines (e.g., radiation therapy machine) that islikely to be used for the patient. With regards to imaging data, thepatient data module 108 may automatically receive new imaging data fromthe imaging engine 102 or, alternatively, may periodically request newimaging data from the imaging engine 102.

When new imaging data associated with a patient is stored in the patientdata store 118, the notification module 110 transmits a notification tothe patient's physician informing the physician that the data isavailable for further evaluation. In one embodiment, the notificationmodule 110 regularly polls the patient data store 118 for new patientdata. In another embodiment, the notification module 110 polls thepatient data store 118 for new patient data when a physician accessesthe treatment planning engine 104 to determine whether new imaging datahas been received for any of the physician's patients. The notificationtransmitted to the physician may be an email, a message transmitted overa short messaging service, or a push notification transmitted to thephysician via a mobile application. The notification may include a linkto automatically launch the contour definition module 112.

The contour definition module 112 detects the contours identifying thethree-dimensional tumor volumes captured in the imaging data and theanatomical structures located in the same region as the tumor volumes.The contour definition module 112 may automatically generate contours ofthe tumor volume and anatomical structures using contouring techniquesknown in the field. In addition, the contour definition module 112 mayautomatically generate contours of the tumor volume and anatomicalstructures using historical contours created for the current patient ora different patient having similar imaging data stored in the patientdata store 118.

The contour definition module 112 may provide suggested contours to thephysician based on previously created contours stored in the patientdata store 118. The contour definition module 112 determines thesuggested contours by comparing the image data associated with thecurrent patient with image data for which contours were previouslydetermined and stored in the patient data store 118. When the image dataassociated with the current patient is statistically similar to imagedata stored in the patient data store 118, the contour definition module112 suggests the previously determined contours for the statisticallysimilar image data to the physician. In operation, the contourdefinition module 112 leverages the outcome recommendation module 114,i.e., the mapping between contours and treatment outcomes, to providethe statistical basis for providing alternate contours for the tumorvolume and the neighboring anatomical structures (also referred toherein as the “planning tumor volume (PTV)”). In this embodiment, thecontour definition module 112 utilizes definitions of the gross tumorvolume (GTV) and/or clinical tumor volume (CTV), where the physician (oralternatively an automated algorithm) contours what is determined withhigh-confidence to be tumor. This GTV or CTV contour is the minimumsuggested PTV size. A treatment outcome is created from this minimumaccepted PTV size based on the mappings between contours and treatmentoutcomes 114. The contour definition module 112 then creates series ofuniform or non-uniform expansions of the PTV and maps these expansionsof the PTV to outcomes based on the outcome results provided by theoutcome recommendation module 114.

To reduce the potential number of contour suggestions, the contourdefinition module 112 may be configured with a threshold such that onlycontours that result in changes to treatment outcomes exceeding thethreshold are presented to the physician. Similarly, if a suggestedcontour results in a treatment outcome that is statistically similar toa contour of a previously treated patient, this contour may be presentedwith an additional indication that it matches a previous patient.

In one embodiment, a metric of similarity may be Euclidean distancebetween predicted treatment outcomes (as output by the outcomerecommendation module 114) and the previously-treated patient outcomes,or other distance-based metrics known in the art. A preferred method tocalculate this distance-based similarity metric, S, is through themethod of least squares between the predicted dose to the new patientbased on each contour expansion, Dnew, and the dose given to a previouspatient, Dprev:

S=√{square root over ((D _(new) −D _(prev))²)}.

In this preferred method, if S is less than the threshold, the contouris recommended. Here, the threshold may be chosen manually (based onphysician-preferred fidelity), or may be learned from the variation inpredicted outcomes across the cross-validated kth model constructed fromthe kth fold of the training data set. If the physician chooses toaccept a suggested contour, then the predicted treatment outcome ispresented to the physician.

The contour definition module 112 also enables the physician to manuallycreate and/or edit the contours of the tumor volume and anatomicalstructures. In operation, the contour definition module 112 provides agraphical user interface that includes a visual representation of theimaging data associated with the patient and one or more contouringtools that allow the physician to create or edit contours. FIG. 2 is anexemplary user interface for defining contours on patient image data, inaccordance with an embodiment of the invention. As shown, the userinterface includes a visual representation 202 of imaging dataassociated with a given patient. The user interface also includescontouring controls 204 that enable a physician to draw the contoursaround tumor volumes and neighboring anatomical structures. In theillustrated example, the region 206 is enclosed by a contour manuallycreated by the physician using the contouring controls 204.

Returning to FIG. 1, as the contour definition module 112 detectscontours (automatically or manually defined), the contour definitionmodule 112 transmits requests to the outcome recommendation module 114to generate recommended treatment outcomes for the current patient basedon the detected contours. The outcome recommendation module 114 performsa multi-feature comparative analysis between the current patient andprevious patients to identify previously-administered treatment outcomesstored in the outcome store 120 that are applicable to the currentpatient.

The outcome store 120 stores information associated with previoustreatment outcomes such as, for example, previously planned radiationtreatments that were approved for use on patients by medical personnel,or previously planned radiation treatments that were used on patients bymedical personnel. In one embodiment, each treatment outcome specifiesthe dose of radiation that was administered to tumor volumes and anyneighboring anatomical structure during the radiation treatment. Inanother embodiment, each treatment outcome specifies a probability ofcontrol for tumor volumes and/or probability of toxicity for anyneighboring anatomical structure resulting from the radiation treatment.The outcome store 120 may also include a medical data database thatincludes medical data associated with the previously planned radiationtreatments. In some embodiments, the outcome store 120 includes aprocessed database configured to store selected data that have beenextracted and transformed from a medical data database and stored in theprocessed database.

The outcome recommendation module 114 compares features extracted frompatient data associated the current patient with features and/oroutcomes extracted from data associated with other patients, as storedin the patient data store 118, to identify recommended treatmentoutcomes for the current patient. The features associated with patientdata may include a physics parameter, a treatment type parameter, apatient image parameter, and/or a disease parameter. In one embodiment,physics parameters may be, or include, penumbra, aperture, incidentangle, beam energy, radiation type, depth of structure, and/or existenceof bolus. Treatment type parameters may be, or include, fractionationschedule, treatment margin, number of beams/arcs, interpretation ofcontours, and/or the clinicians who are part of the team creating theradiation treatment outcome. Patient image parameters may be, orinclude, distance, volume, geometric relationship, and/or the importanceof structures and surrounding structures. Disease parameters may be, orinclude, disease stage, prior or post treatment therapy, prior radiationtherapy, prior radiation damage to nearby tissue, disease type, diseasehistology, extent of the disease, and/or prior disease.

Any number of different types of techniques and/or algorithms may beutilized to identify the recommended treatment outcomes by comparingfeatures of the current patient with previous patients, and may includestatistical techniques, pattern-matching techniques, artificialintelligence techniques, and/or the like. In some embodiments, theoutcome recommendation module 114 may include a search engine, a querymodule, and/or a database management component. Identifying previouslyadministered treatment outcomes based on a multi-feature comparativeanalysis, as performed by the outcome recommendation module 114 may beperformed with the techniques described above, and is further describedin U.S. patent application Ser. No. 14/310,925, filed on Jun. 20, 2014,which is hereby incorporated by reference in its entirety.

The outcome recommendation module 114 presents the recommended treatmentoutcomes for the current patient to the physician via the user interface(UI) module 116. The UI module 116 generates a visual representation ofeach recommended treatment outcome. In one embodiment, the UI module 116associates different visual indicators with the radiation dosesdelivered during the execution of the treatment outcome. As discussedabove, each treatment outcome specifies the dose of radiation treatmentdelivered to one or more tumor volumes and nearby anatomical structures.For each tumor volume or anatomical structure, the UI module 116determines a visual indicator for the corresponding dose based on therisk level associated with the dose. When the treatment outcomespecifies a dose that delivers a larger than recommended amount ofradiation to an organ at risk, the visual representation of the dose tothat organ may be highlighted or color coded to represent the high risk.Conversely, when the treatment outcome specifies a dose that delivers anacceptable amount of radiation to an organ at risk, the visualrepresentation of the dose to that organ may be highlighted or colorcoded to represent the relatively lower risk. Such visual indicatorsenable the physician viewing and evaluating the recommended treatmentoutcomes to quickly determine whether a given treatment outcome isoptimal for the current patient.

FIG. 3 is an exemplary user interface generated by the UI module 116 forproviding visual representations of recommended treatment outcomes, inaccordance with an embodiment of the invention. As shown, the userinterface includes a structure column 302 that lists each of thestructures and tumors identified by the contour. The user interface alsoincludes one or more outcome columns, such as outcome column 304 andoutcome column 306. Each outcome column is associated with a differentrecommended treatment outcome and lists, for each structure and tumor inthe structure column 302, the dose of radiation, if any, to be deliveredper the treatment outcome. Further, as illustrated, the doses ofradiation in the outcome columns are color based on the risk levelassociated with the dose.

Returning to FIG. 2, upon evaluating the recommended treatment outcomes,the physician may select one of the recommended treatment outcomes via acontrol provided by the user interface module 116. Alternatively, whennone of the recommended outcomes is deemed optimal by the physician, thephysician may edit the contours of the tumor volume and neighboringanatomical structures via the contour definition module 112. In responseto the contours being edited, the contour definition module 112transmits another request to the outcome recommendation module 114 togenerate new recommended treatment outcomes that are subsequentlypresented to the physician.

In such a paradigm, the physician is able to adjust contours and inreal-time evaluate the impact on the tumor volume and the toxicity riskto the nearby anatomical structures. When the physician is satisfiedwith a given recommended outcome, the physician selects the treatmentoutcome for the current patient. In response, the outcome recommendationmodule 114 stores the contour in conjunction with the selected treatmentoutcome in the patient data store 118. In addition, the outcomerecommendation module 114 transmits a notification to the treatmentdelivery engine 106 indicating that the physician has selected atreatment outcome for the patient.

The treatment delivery engine 106 enables a dosimetrist or physicist tocreate a patient-specific treatment delivery plan based on the treatmentoutcome selected by the physician (“the selected treatment outcome”). Inoperation, the treatment delivery engine 106 transmits a notification,such as an email or a push notification on a mobile application, to thedosimetrist or physicist indicating that the physician selected atreatment outcome for the patient. The notification may optionallyinclude a representation of the selected treatment outcome and/or a linkto access the selected treatment outcome. The dosimetrist or physicistevaluates the selected treatment outcome and creates a treatmentdelivery plan that is specific to the patient. Specifically, theselected treatment outcome informs the dosimetrist or physicist of theclinically appropriate profile of radiation to be delivered includingbut not limited to radiation intensity, angle of delivery, multi-leafcollimator status, temporal fractionation, anatomy, presentation of thetumor, anticipated treatment outcomes, prior clinical staff, andanticipated treatment course.

Based on the treatment delivery plan, the treatment delivery engine 106generates a patient-specific delivery template that configures aradiation therapy machine for delivering the radiation treatment to thepatient. In one embodiment, the treatment delivery engine 106 interactswith a therapy machine control interface that is configured withstandard communication protocols. The patient-specific delivery templateidentifies the tumor volumes as well as the anatomical structures thatare to receive radiation treatment. For each volume or structure, thedelivery template may also specify the percentage volume that is toreceive radiation treatment and the dose of treatment to be delivered.In addition, this template may specify the optimization objectives,treatment protocols, beam orientations, collimator/multi-leaf collimatorpositions, couch positions, and other parameters known in the art. Table1 illustrates an exemplary patient-specific delivery template.

TABLE 1 Dose Type of Dose Critical Structures Volume constraintConstraint Priority PTV 63 95.39 Lower 63 120.0 PTV 60 93.97 Lower 60120.0 PTV 54 95.22 Lower 54 120.0 IPSeyemax 0 Upper 1.78 50.0CONTRAeyemax 0 Upper 1.5 50.0 IPSOPTICNRVmax 0 Upper 2.13 50.0CONTRAOPTICNRVmax 0 Upper 1.5 50.0 TS_IPSPAROTIDmax 0 Upper 66.86 90.0TS_CONTRAPAROTIDmax 0 Upper 59.19 90.0 IPSMIDEARmean Mean 8.3 50.0CONTRAMIDEARmean Mean 4.86 50.0 IPSPAROTIDmean Mean 29.68 120.0CONTRAPAROTIDmean Mean 15.66 90.0 IPSLOBEmean Mean 2.38 50.0CONTRALOBEmean Mean 1.63 50.0 CONTRASALVmean Mean 35.63 90.0

Physician Directed Treatment Planning

The treatment planning engine 104 enables physician directed treatmentplanning where the physician is able to adjust contours around the tumorvolume and in real-time or near real-time evaluate the impact on thetumor volume and the toxicity risk to the nearby anatomical structures.FIG. 4 is a state diagram 400 illustrating the various stages ofphysician directed radiation treatment planning using the treatmentplanning engine 104, in accordance with an embodiment of the invention.In state 402, patient data associated with a patient who is to receiveradiation treatment is collected. Such patient data may include imagingdata, electronic medical records and information related to theanticipated therapy system. In state 404, the patient's physician, usingthe contour definition module 112, defines the contours of the tumorvolumes and/or the anatomical structures included in the imaging datathat are to receive radiation treatment. In state 406, the physicianevaluates recommended treatment outcomes recommended by the outcomerecommendation engine 114 to determine whether any of the treatmentoutcomes are suitable for the patient. The physician iterates throughstates 404 and 406 until the outcome recommendation engine 114 presentsa suitable treatment outcome. In state 408, the physician selects thetreatment outcome for the patient via the outcome recommendation engine114, which transmits a notification to the dosimetrist. In state 410,the dosimetrist evaluates the selected treatment outcome and creates atreatment delivery plan that is specific to the patient. In state 412, aradiation therapy machine delivers radiation treatment to the patientbased on the delivery plan created by the dosimetrist.

FIG. 5 is a flow diagram 500 illustrating the steps for physiciandirected radiation treatment planning, in accordance with an embodimentof the invention. Other embodiments may perform the steps of the processillustrated in FIG. 5 in different orders and can include different,additional and/or fewer steps. The process may be performed by anysuitable entity, such as the treatment planning engine 104.

The treatment planning engine 104 receives 502 patient data, including,but not limited to, imaging data, data accumulated from the medicalrecord, associated with a patient, and information related to theanticipated therapy system. The treatment planning engine 104 mayautomatically receive imaging data from the imaging engine 102 or,alternatively, may periodically request imaging data from the imagingengine 102. Imaging data associated with a patient includes visualrepresentations of the interior of a patient's body or a portionthereof. The treatment planning engine 104 detects 504, based on theimaging data, contours identifying the three-dimensional tumor volumesand the anatomical structures near the tumor volumes. In one embodiment,the treatment planning engine 104 enables the patient's physician tocreate and/or edit the contours of the tumor volume and anatomicalstructures via a graphical user interface. In alternate embodiments, thetreatment planning engine 104 automatically creates the contours usingpre-existing contouring techniques.

The treatment planning engine 104 recommends 506 treatment outcomes forthe patient based on the detected contours. In one embodiment, thetreatment planning engine 104 performs a multi-feature comparativeanalysis between the current patient and previous patients to identifytreatment outcomes stored in the outcome store 120 that are applicableto the current patient. The treatment planning engine 104 presents 508the recommended treatment outcomes to the physician via a user interfacethat generates a visual representation of each recommended treatmentoutcome.

The treatment planning engine 104 determines 510 whether the contouridentifying the three-dimensional tumor volumes and the anatomicalstructures has changed. Specifically, the physician may edit the contourusing one or more contouring tools. If the contour changes, then thetreatment planning engine 104 re-computes 506 new treatment outcomerecommendations based on the updated contours. If the contour does notchange, then the treatment planning engine 104 determines 512 whetherthe physician selected a given recommended treatment outcome. Thetreatment planning engine 104 continues to loop through 510-512 untilthe physician selects a plan.

When the patient selects a treatment outcome, the treatment planningengine 104 provides 514 the selected treatment outcome to the treatmentdelivery engine 106 for the purposes of delivering radiation treatmentto the patient according to the selected outcome. The treatment deliveryengine 106 enables a dosimetrist or physicist to create apatient-specific treatment delivery plan based on the treatment outcomeselected by the physician.

Example Computer System

FIG. 6 is a block diagram illustrating components of an example machineable to read instructions from a machine-readable medium and executethem in a processor (or controller). The computer system 600 can be usedto execute instructions 624 (e.g., program code or software) for causingthe machine to perform any one or more of the methodologies (orprocesses) described herein. In alternative embodiments, the machineoperates as a standalone device or a connected (e.g., networked) devicethat connects to other machines. In a networked deployment, the machinemay operate in the capacity of a server machine or a client machine in aserver-client network environment, or as a peer machine in apeer-to-peer (or distributed) network environment. Each of the variousengines and modules described herein may be implemented using all orsome of the components of the computer system 600.

The machine may be a server computer, a client computer, a personalcomputer (PC), a tablet PC, a set-top box (STB), a smartphone, aninternet of things (IoT) appliance, a network router, switch or bridge,or any machine capable of executing instructions 624 (sequential orotherwise) that specify actions to be taken by that machine. Further,while only a single machine is illustrated, the term “machine” shallalso be taken to include any collection of machines that individually orjointly execute instructions 624 to perform any one or more of themethodologies discussed herein.

The example computer system 600 includes one or more processing units(generally processor 602). The processor 602 is, for example, a centralprocessing unit (CPU), a graphics processing unit (GPU), a digitalsignal processor (DSP), a controller, a state machine, one or moreapplication specific integrated circuits (ASICs), one or moreradio-frequency integrated circuits (RFICs), or any combination ofthese. The computer system 600 also includes a main memory 604. Thecomputer system may include a storage unit 616. The processor 602,memory 604 and the storage unit 616 communicate via a bus 608.

In addition, the computer system 600 can include a static memory 606, adisplay driver 660 (e.g., to drive a plasma display panel (PDP), aliquid crystal display (LCD), or a projector). The computer system 600may also include an alphanumeric input device 662 (e.g., a keyboard), acursor control device 614 (e.g., a mouse, a trackball, a joystick, amotion sensor, or other pointing instrument), a signal generation device618 (e.g., a speaker), and a network interface device 620, which alsoare configured to communicate via the bus 608.

The storage unit 616 includes a machine-readable medium 622 on which isstored instructions 624 (e.g., software) embodying any one or more ofthe methodologies or functions described herein. The instructions 624may also reside, completely or at least partially, within the mainmemory 604 or within the processor 602 (e.g., within a processor's cachememory) during execution thereof by the computer system 600, the mainmemory 604 and the processor 602 also constituting machine-readablemedia. The instructions 624 may be transmitted or received over anetwork 626 via the network interface device 620.

While machine-readable medium 622 is shown in an example embodiment tobe a single medium, the term “machine-readable medium” should be takento include a single medium or multiple media (e.g., a centralized ordistributed database, or associated caches and servers) able to storethe instructions 624. The term “machine-readable medium” shall also betaken to include any medium that is capable of storing instructions 624for execution by the machine and that cause the machine to perform anyone or more of the methodologies disclosed herein. The term“machine-readable medium” includes, but not be limited to, datarepositories in the form of solid-state memories, optical media, andmagnetic media.

Conclusion

The foregoing description of the embodiments of the invention has beenpresented for the purpose of illustration; it is not intended to beexhaustive or to limit the invention to the precise forms disclosed.Persons skilled in the relevant art can appreciate that manymodifications and variations are possible in light of the abovedisclosure.

Some portions of this description describe the embodiments of theinvention in terms of algorithms and symbolic representations ofoperations on information. These algorithmic descriptions andrepresentations are commonly used by those skilled in the dataprocessing arts to convey the substance of their work effectively toothers skilled in the art. These operations, while describedfunctionally, computationally, or logically, are understood to beimplemented by computer programs or equivalent electrical circuits,microcode, or the like. Furthermore, it has also proven convenient attimes, to refer to these arrangements of operations as modules, withoutloss of generality. The described operations and their associatedmodules may be embodied in software, firmware, hardware, or anycombinations thereof.

Any of the steps, operations, or processes described herein may beperformed or implemented with one or more hardware or software modules,alone or in combination with other devices. In one embodiment, asoftware module is implemented with a computer program productcomprising a computer-readable medium containing computer program code,which can be executed by a computer processor for performing any or allof the steps, operations, or processes described.

Embodiments of the invention may also relate to an apparatus forperforming the operations herein. This apparatus may be speciallyconstructed for the required purposes, and/or it may comprise ageneral-purpose computing device selectively activated or reconfiguredby a computer program stored in the computer. Such a computer programmay be stored in a non-transitory, tangible computer readable storagemedium, or any type of media suitable for storing electronicinstructions, which may be coupled to a computer system bus.Furthermore, any computing systems referred to in the specification mayinclude a single processor or may be architectures employing multipleprocessor designs for increased computing capability.

Embodiments of the invention may also relate to a product that isproduced by a computing process described herein. Such a product maycomprise information resulting from a computing process, where theinformation is stored on a non-transitory, tangible computer readablestorage medium and may include any embodiment of a computer programproduct or other data combination described herein.

Finally, the language used in the specification has been principallyselected for readability and instructional purposes, and it may not havebeen selected to delineate or circumscribe the inventive subject matter.It is therefore intended that the scope of the invention be limited notby this detailed description, but rather by any claims that issue on anapplication based hereon. Accordingly, the disclosure of the embodimentsof the invention is intended to be illustrative, but not limiting, ofthe scope of the invention, which is set forth in the following claims.

What is claimed is:
 1. A method for creating a radiation treatment planfor a given patient, the method comprising: receiving imaging data thatincludes a representation of an interior of a patient who is to receiveradiation treatment; repeatedly performing a treatment planning processcomprising: detecting a contour identifying one or more anatomicalstructures and at least one tumor volume represented by the imagingdata, computing a set of recommended treatment outcomes for the patientbased on the contour and previously administered treatment outcomes, andpresenting in a user interface the set of recommended treatment outcomesto a physician for evaluation; receiving a selection from the physicianof a given recommended treatment outcome; and providing the selectedtreatment outcome to a system for generating a treatment plan for thepatient that achieves the selected outcome.
 2. The method of claim 1,wherein detecting the contour comprises determining that apreviously-detected contour has been edited and detecting a new contouridentifying one or more anatomical structures and at least one tumorvolume represented by the imaging data.
 3. The method of claim 1,wherein detecting the contour comprises providing a contouring interfaceto a physician that enables the physician to manually define one or morecontours on a visual representation of the imaging data.
 4. The methodof claim 1, wherein detecting the contour comprises automaticallydetermining the contour based on a previously-created contour associatedwith second imaging data.
 5. The method of claim 1, wherein detectingthe contour comprises: generating one or more expansions of a minimumtumor volume definition provided by the physician; identifying a subsetof the previously administered treatment outcomes that map to at leastone of the one or more expansions of the minimum tumor volume; andsuggesting an alternative contour associated with a first treatmentoutcome included in the subset of the previously administered treatmentoutcomes.
 6. The method of claim 5, wherein suggesting the alternativecontour comprises: comparing the first treatment outcome with arecommended treatment outcome previously presented in the userinterface; determining that a difference between the first treatmentoutcome and the recommended treatment outcome is greater than athreshold difference; and selecting the first treatment outcome forcontour suggestion
 7. The method of claim 1, wherein computing the setof recommended treatment outcomes comprises comparing features extractedfrom patient data associated the patient with features extracted fromtreatment outcomes previously delivered to other patients.
 8. The methodof claim 1, wherein the recommended treatment outcome specifies a doseof radiation to be delivered to the patient.
 9. The method of claim 1,wherein the system for generating the treatment plan is operated by atechnician or dosimetrist.
 10. The method of claim 1, wherein presentingthe set of recommended treatment outcomes comprises, for eachrecommended treatment outcome, generating a visual representation thatspecifies a different radiation dose to be delivered to each of one ormore tumor volumes and/or anatomical structures.
 11. The method of claim10, wherein generating the visual representation for a given treatmentoutcome comprises presenting a visual indicator associated with eachradiation dose based on the risk level associated with the dose.
 12. Themethod of claim 1, further comprising transmitting a notification to thephysician when the imaging data is received, the notification includinga mechanism for initiating repeatedly performing the treatment planningprocess.
 13. The method of claim 1, wherein providing the selectedtreatment outcome for delivering radiation treatment comprisesgenerating a patient-specific delivery template that configures aradiation therapy machine for delivering radiation treatment to thepatient.
 14. A computer program product for creating a radiationtreatment plan for a given patient, the computer program productcomprising a computer-readable storage medium containing computerprogram code for: receiving imaging data that includes a representationof an interior of a patient who is to receive radiation treatment;repeatedly performing a treatment planning process comprising: detectinga contour identifying one or more anatomical structures and at least onetumor volume represented by the imaging data, computing a set ofrecommended treatment outcomes for the patient based on the contour andpreviously administered treatment outcomes, and presenting in a userinterface the set of recommended treatment outcomes to a physician forevaluation; receiving a selection from the physician of a givenrecommended treatment outcome; and providing the selected treatmentoutcome to a system for generating a treatment plan for the patient thatachieves the selected outcome.
 15. The computer program product of claim14, wherein detecting the contour comprises determining that apreviously-detected contour has been edited and detecting a new contouridentifying one or more anatomical structures and at least one tumorvolume represented by the imaging data.
 16. The computer program productof claim 14, wherein detecting the contour comprises providing acontouring interface to a physician that enables the physician tomanually define one or more contours on a visual representation of theimaging data.
 17. The computer program product of claim 14, whereindetecting the contour comprises automatically determining the contourbased on a previously-created contour associated with second imagingdata.
 18. The computer program product of claim 14, wherein detectingthe contour comprises: generating one or more expansions of a minimumtumor volume definition provided by the physician; identifying a subsetof the previously administered treatment outcomes that map to at leastone of the one or more expansions of the minimum tumor volume; andsuggesting an alternative contour associated with a first treatmentoutcome included in the subset of the previously administered treatmentoutcomes.
 19. The computer program product of claim 18, whereinsuggesting the alternative contour comprises: comparing the firsttreatment outcome with a recommended treatment outcome previouslypresented in the user interface; and determining that a differencebetween the first treatment outcome and the recommended treatmentoutcome is greater than a threshold difference.
 20. The computer programproduct of claim 14, wherein computing the set of recommended treatmentoutcomes comprises comparing features extracted from patient dataassociated the patient with features extracted from treatment outcomespreviously delivered to other patients.
 21. The computer program productof claim 14, wherein the recommended treatment outcome specifies a doseof radiation to be delivered to the patient.
 22. The computer programproduct of claim 14, wherein the system for generating the treatmentplan is operated by a technician or dosimetrist.
 23. The computerprogram product of claim 14, wherein presenting the set of recommendedtreatment outcomes comprises, for each recommended treatment outcome,generating a visual representation that specifies a different radiationdose to be delivered to each of one or more tumor volumes and/oranatomical structures.
 24. The computer program product of claim 23,wherein generating the visual representation for a given treatmentoutcome comprises presenting a visual indicator associated with eachradiation dose based on the risk level associated with the dose.
 25. Thecomputer program product of claim 14, wherein providing the selectedtreatment outcome for delivering radiation treatment comprisesgenerating a patient-specific delivery template that configures aradiation therapy machine for delivering radiation treatment to thepatient.
 26. A system for creating a radiation treatment plan for agiven patient, the system comprising: a patient data module configuredto receive imaging data that includes a representation of an interior ofa patient who is to receive radiation treatment; a contour detectionmodule configured to repeatedly detect a contour identifying one or moreanatomical structures and at least one tumor volume represented by theimaging data; and an outcome recommendation module configured to:repeatedly compute a set of recommended treatment outcomes for thepatient based on the contour and previously administered treatmentoutcomes, receive a selection from a physician of a given recommendedtreatment outcome, and provide the selected treatment outcome to asystem for generating a treatment plan for the patient that achieves theselected outcome.